Content-grounded dialogue evaluation for Arabic remains under-resourced, particularly across Modern Standard (MSA), Egyptian, and Maghrebi varieties. We introduce Shawarma Chats, a benchmark of 30,000 six-turn conversations grounded in Wikipedia content, evenly split across the three dialects. To build this corpus, we prompt five frontier LLMs GPT-4o, Gemini 2.5 Flash, Qwen-Plus, DeepSeek-Chat, and Mistral Large to generate 1,500 seed dialogues. Native Arabic speakers evaluate these outputs to select the most effective generator and most human-aligned grader. Sub-A dialogues undergo a two-pass, rationale-driven self-repair loop where the grader critiques and the generator revises; unresolved cases are manually corrected. We apply this pipeline to 10,000 Wikipedia paragraphs to create 30,000 high-quality conversations 10,000 per dialect—at modest human cost. To validate the benchmark, we LoRA-fine-tune six open LLMs (1–24 B parameters) on Shawarma Chats and observe consistent gains in automatic-grader scores, BERTScore, BLEU and ROUGE particularly for models larger than 7 B parameters. Shawarma Chats thus establishes the first large-scale, dialect-aware, content-grounded dialogue benchmark for Arabic.